Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells374017
Missing cells (%)7.4%
Total size in memory215.4 MiB
Average record size in memory2.2 KiB

Variable types

Numeric13
Text37

Alerts

examide has constant value ""Constant
citoglipton has constant value ""Constant
race has 2273 (2.2%) missing valuesMissing
weight has 98569 (96.9%) missing valuesMissing
payer_code has 40256 (39.6%) missing valuesMissing
medical_specialty has 49949 (49.1%) missing valuesMissing
diag_3 has 1423 (1.4%) missing valuesMissing
max_glu_serum has 96420 (94.7%) missing valuesMissing
A1Cresult has 84748 (83.3%) missing valuesMissing
number_emergency is highly skewed (γ1 = 22.85558215)Skewed
encounter_id has unique valuesUnique
num_procedures has 46652 (45.8%) zerosZeros
number_outpatient has 85027 (83.6%) zerosZeros
number_emergency has 90383 (88.8%) zerosZeros
number_inpatient has 67630 (66.5%) zerosZeros

Reproduction

Analysis started2023-11-22 16:18:54.080791
Analysis finished2023-11-22 16:18:56.861926
Duration2.78 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ)

UNIQUE 

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165201645.6
Minimum12522
Maximum443867222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:56.993410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median152388987
Q3230270887.5
95-th percentile378962843
Maximum443867222
Range443854700
Interquartile range (IQR)145309693.5

Descriptive statistics

Standard deviation102640296
Coefficient of variation (CV)0.6213031087
Kurtosis-0.1020713932
Mean165201645.6
Median Absolute Deviation (MAD)70921143
Skewness0.6991415513
Sum1.681191067 × 1013
Variance1.053503036 × 1016
MonotonicityNot monotonic
2023-11-22T17:18:57.138679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2278392 1
 
< 0.1%
190792044 1
 
< 0.1%
190790070 1
 
< 0.1%
190789722 1
 
< 0.1%
190786806 1
 
< 0.1%
190785018 1
 
< 0.1%
190781412 1
 
< 0.1%
190775886 1
 
< 0.1%
190764504 1
 
< 0.1%
190760322 1
 
< 0.1%
Other values (101756) 101756
> 99.9%
ValueCountFrequency (%)
12522 1
< 0.1%
15738 1
< 0.1%
16680 1
< 0.1%
28236 1
< 0.1%
35754 1
< 0.1%
ValueCountFrequency (%)
443867222 1
< 0.1%
443857166 1
< 0.1%
443854148 1
< 0.1%
443847782 1
< 0.1%
443847548 1
< 0.1%

patient_nbr
Real number (ℝ)

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330400.69
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:57.283554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.75
Q123413221
median45505143
Q387545949.75
95-th percentile111480273
Maximum189502619
Range189502484
Interquartile range (IQR)64132728.75

Descriptive statistics

Standard deviation38696359.35
Coefficient of variation (CV)0.7122413759
Kurtosis-0.3473720444
Mean54330400.69
Median Absolute Deviation (MAD)32950134
Skewness0.4712807224
Sum5.528987557 × 1012
Variance1.497408227 × 1015
MonotonicityNot monotonic
2023-11-22T17:18:57.449777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 40
 
< 0.1%
43140906 28
 
< 0.1%
1660293 23
 
< 0.1%
88227540 23
 
< 0.1%
23199021 23
 
< 0.1%
23643405 22
 
< 0.1%
84428613 22
 
< 0.1%
92709351 21
 
< 0.1%
88789707 20
 
< 0.1%
29903877 20
 
< 0.1%
Other values (71508) 101524
99.8%
ValueCountFrequency (%)
135 2
< 0.1%
378 1
< 0.1%
729 1
< 0.1%
774 1
< 0.1%
927 1
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189481478 1
< 0.1%
189445127 1
< 0.1%
189365864 1
< 0.1%
189351095 1
< 0.1%

race
Text

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2273
Missing (%)2.2%
Memory size6.4 MiB
2023-11-22T17:18:57.615508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length9
Mean length10.05168203
Min length5

Characters and Unicode

Total characters1000072
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowAfricanAmerican
4th rowCaucasian
5th rowCaucasian
ValueCountFrequency (%)
caucasian 76099
76.5%
africanamerican 19210
 
19.3%
hispanic 2037
 
2.0%
other 1506
 
1.5%
asian 641
 
0.6%
2023-11-22T17:18:57.986013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.7%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (7) 47012
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 881369
88.1%
Uppercase Letter 118703
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269395
30.6%
i 119234
13.5%
n 117197
13.3%
c 116556
13.2%
s 78777
 
8.9%
u 76099
 
8.6%
r 39926
 
4.5%
e 20716
 
2.4%
f 19210
 
2.2%
m 19210
 
2.2%
Other values (3) 5049
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 76099
64.1%
A 39061
32.9%
H 2037
 
1.7%
O 1506
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1000072
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.7%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (7) 47012
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.7%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (7) 47012
 
4.7%

gender
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2023-11-22T17:18:58.096062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length6
Mean length5.075496728
Min length4

Characters and Unicode

Total characters516513
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale
ValueCountFrequency (%)
female 54708
53.8%
male 47055
46.2%
unknown/invalid 3
 
< 0.1%
2023-11-22T17:18:58.331177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 414741
80.3%
Uppercase Letter 101769
 
19.7%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 156471
37.7%
a 101766
24.5%
l 101766
24.5%
m 54708
 
13.2%
n 12
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
w 3
 
< 0.1%
v 3
 
< 0.1%
i 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 54708
53.8%
M 47055
46.2%
U 3
 
< 0.1%
I 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 516510
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (5) 15
 
< 0.1%
Common
ValueCountFrequency (%)
/ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

age
Text

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2023-11-22T17:18:58.477119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.025863255
Min length6

Characters and Unicode

Total characters714994
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[10-20)
3rd row[20-30)
4th row[30-40)
5th row[40-50)
ValueCountFrequency (%)
70-80 26068
25.6%
60-70 22483
22.1%
50-60 17256
17.0%
80-90 17197
16.9%
40-50 9685
 
9.5%
30-40 3775
 
3.7%
90-100 2793
 
2.7%
20-30 1657
 
1.6%
10-20 691
 
0.7%
0-10 161
 
0.2%
2023-11-22T17:18:58.726835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 409696
57.3%
Open Punctuation 101766
 
14.2%
Dash Punctuation 101766
 
14.2%
Close Punctuation 101766
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206325
50.4%
7 48551
 
11.9%
8 43265
 
10.6%
6 39739
 
9.7%
5 26941
 
6.6%
9 19990
 
4.9%
4 13460
 
3.3%
3 5432
 
1.3%
1 3645
 
0.9%
2 2348
 
0.6%
Open Punctuation
ValueCountFrequency (%)
[ 101766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101766
100.0%
Close Punctuation
ValueCountFrequency (%)
) 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 714994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 714994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

weight
Text

MISSING 

Distinct9
Distinct (%)0.3%
Missing98569
Missing (%)96.9%
Memory size3.2 MiB
2023-11-22T17:18:58.852626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.910541132
Min length4

Characters and Unicode

Total characters25290
Distinct characters9
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[75-100)
2nd row[50-75)
3rd row[0-25)
4th row[75-100)
5th row[75-100)
ValueCountFrequency (%)
75-100 1336
41.8%
50-75 897
28.1%
100-125 625
19.5%
125-150 145
 
4.5%
25-50 97
 
3.0%
0-25 48
 
1.5%
150-175 35
 
1.1%
175-200 11
 
0.3%
200 3
 
0.1%
2023-11-22T17:18:59.101618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5172
20.5%
5 4368
17.3%
[ 3194
12.6%
- 3194
12.6%
) 3194
12.6%
1 2957
11.7%
7 2279
9.0%
2 929
 
3.7%
> 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15705
62.1%
Open Punctuation 3194
 
12.6%
Dash Punctuation 3194
 
12.6%
Close Punctuation 3194
 
12.6%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5172
32.9%
5 4368
27.8%
1 2957
18.8%
7 2279
14.5%
2 929
 
5.9%
Open Punctuation
ValueCountFrequency (%)
[ 3194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3194
100.0%
Math Symbol
ValueCountFrequency (%)
> 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5172
20.5%
5 4368
17.3%
[ 3194
12.6%
- 3194
12.6%
) 3194
12.6%
1 2957
11.7%
7 2279
9.0%
2 929
 
3.7%
> 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5172
20.5%
5 4368
17.3%
[ 3194
12.6%
- 3194
12.6%
) 3194
12.6%
1 2957
11.7%
7 2279
9.0%
2 929
 
3.7%
> 3
 
< 0.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024006053
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:59.208117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44540283
Coefficient of variation (CV)0.7141296972
Kurtosis1.942476114
Mean2.024006053
Median Absolute Deviation (MAD)0
Skewness1.591984327
Sum205975
Variance2.08918934
MonotonicityNot monotonic
2023-11-22T17:18:59.315507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 53990
53.1%
3 18869
 
18.5%
2 18480
 
18.2%
6 5291
 
5.2%
5 4785
 
4.7%
8 320
 
0.3%
7 21
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1 53990
53.1%
2 18480
 
18.2%
3 18869
 
18.5%
4 10
 
< 0.1%
5 4785
 
4.7%
ValueCountFrequency (%)
8 320
 
0.3%
7 21
 
< 0.1%
6 5291
5.2%
5 4785
4.7%
4 10
 
< 0.1%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.715641766
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:59.436461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.280165509
Coefficient of variation (CV)1.421064204
Kurtosis6.003346764
Mean3.715641766
Median Absolute Deviation (MAD)0
Skewness2.563066993
Sum378126
Variance27.88014781
MonotonicityNot monotonic
2023-11-22T17:18:59.558159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 60234
59.2%
3 13954
 
13.7%
6 12902
 
12.7%
18 3691
 
3.6%
2 2128
 
2.1%
22 1993
 
2.0%
11 1642
 
1.6%
5 1184
 
1.2%
25 989
 
1.0%
4 815
 
0.8%
Other values (16) 2234
 
2.2%
ValueCountFrequency (%)
1 60234
59.2%
2 2128
 
2.1%
3 13954
 
13.7%
4 815
 
0.8%
5 1184
 
1.2%
ValueCountFrequency (%)
28 139
 
0.1%
27 5
 
< 0.1%
25 989
1.0%
24 48
 
< 0.1%
23 412
0.4%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.754436649
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:59.667062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.064080834
Coefficient of variation (CV)0.7062517293
Kurtosis1.744989372
Mean5.754436649
Median Absolute Deviation (MAD)0
Skewness1.029934878
Sum585606
Variance16.51675303
MonotonicityNot monotonic
2023-11-22T17:18:59.774958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 57494
56.5%
1 29565
29.1%
17 6781
 
6.7%
4 3187
 
3.1%
6 2264
 
2.2%
2 1104
 
1.1%
5 855
 
0.8%
3 187
 
0.2%
20 161
 
0.2%
9 125
 
0.1%
Other values (7) 43
 
< 0.1%
ValueCountFrequency (%)
1 29565
29.1%
2 1104
 
1.1%
3 187
 
0.2%
4 3187
 
3.1%
5 855
 
0.8%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 12
 
< 0.1%
20 161
 
0.2%
17 6781
6.7%
14 2
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395986872
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:18:59.873912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.985107767
Coefficient of variation (CV)0.6790529304
Kurtosis0.8502508405
Mean4.395986872
Median Absolute Deviation (MAD)2
Skewness1.133998719
Sum447362
Variance8.910868383
MonotonicityNot monotonic
2023-11-22T17:18:59.979489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 17756
17.4%
2 17224
16.9%
1 14208
14.0%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
Other values (4) 5555
 
5.5%
ValueCountFrequency (%)
1 14208
14.0%
2 17224
16.9%
3 17756
17.4%
4 13924
13.7%
5 9966
9.8%
ValueCountFrequency (%)
14 1042
1.0%
13 1210
1.2%
12 1448
1.4%
11 1855
1.8%
10 2342
2.3%

payer_code
Text

MISSING 

Distinct17
Distinct (%)< 0.1%
Missing40256
Missing (%)39.6%
Memory size4.7 MiB
2023-11-22T17:19:00.083125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters123020
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMC
2nd rowMC
3rd rowMC
4th rowMC
5th rowMC
ValueCountFrequency (%)
mc 32439
52.7%
hm 6274
 
10.2%
sp 5007
 
8.1%
bc 4655
 
7.6%
md 3532
 
5.7%
cp 2533
 
4.1%
un 2448
 
4.0%
cm 1937
 
3.1%
og 1033
 
1.7%
po 592
 
1.0%
Other values (7) 1060
 
1.7%
2023-11-22T17:19:00.283357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
U 2448
 
2.0%
N 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 123020
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
U 2448
 
2.0%
N 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 123020
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
U 2448
 
2.0%
N 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
U 2448
 
2.0%
N 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%

medical_specialty
Text

MISSING 

Distinct72
Distinct (%)0.1%
Missing49949
Missing (%)49.1%
Memory size5.1 MiB
2023-11-22T17:19:00.444747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length33
Mean length15.95090414
Min length6

Characters and Unicode

Total characters826528
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd rowInternalMedicine
3rd rowFamily/GeneralPractice
4th rowFamily/GeneralPractice
5th rowCardiology
ValueCountFrequency (%)
internalmedicine 14635
28.2%
emergency/trauma 7565
14.6%
family/generalpractice 7440
14.4%
cardiology 5352
 
10.3%
surgery-general 3099
 
6.0%
nephrology 1613
 
3.1%
orthopedics 1400
 
2.7%
orthopedics-reconstructive 1233
 
2.4%
radiologist 1140
 
2.2%
pulmonology 871
 
1.7%
Other values (62) 7469
14.4%
2023-11-22T17:19:00.739116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 105151
12.7%
r 76899
 
9.3%
a 71149
 
8.6%
n 68798
 
8.3%
i 63308
 
7.7%
c 50007
 
6.1%
l 48871
 
5.9%
y 34937
 
4.2%
t 34149
 
4.1%
o 34053
 
4.1%
Other values (33) 239206
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 705846
85.4%
Uppercase Letter 98148
 
11.9%
Other Punctuation 15907
 
1.9%
Dash Punctuation 6627
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 105151
14.9%
r 76899
10.9%
a 71149
10.1%
n 68798
9.7%
i 63308
9.0%
c 50007
7.1%
l 48871
6.9%
y 34937
 
4.9%
t 34149
 
4.8%
o 34053
 
4.8%
Other values (13) 118524
16.8%
Uppercase Letter
ValueCountFrequency (%)
M 15055
15.3%
I 14683
15.0%
G 11882
12.1%
P 10448
10.6%
T 8332
8.5%
E 7861
8.0%
F 7451
7.6%
C 6307
6.4%
S 5156
 
5.3%
O 4146
 
4.2%
Other values (7) 6827
7.0%
Other Punctuation
ValueCountFrequency (%)
/ 15871
99.8%
& 36
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 6627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 803994
97.3%
Common 22534
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 105151
13.1%
r 76899
 
9.6%
a 71149
 
8.8%
n 68798
 
8.6%
i 63308
 
7.9%
c 50007
 
6.2%
l 48871
 
6.1%
y 34937
 
4.3%
t 34149
 
4.2%
o 34053
 
4.2%
Other values (30) 216672
26.9%
Common
ValueCountFrequency (%)
/ 15871
70.4%
- 6627
29.4%
& 36
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 826528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 105151
12.7%
r 76899
 
9.3%
a 71149
 
8.6%
n 68798
 
8.3%
i 63308
 
7.7%
c 50007
 
6.1%
l 48871
 
5.9%
y 34937
 
4.2%
t 34149
 
4.1%
o 34053
 
4.1%
Other values (33) 239206
28.9%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09564098
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:00.860165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.67436225
Coefficient of variation (CV)0.4565278947
Kurtosis-0.2450735189
Mean43.09564098
Median Absolute Deviation (MAD)13
Skewness-0.2365439206
Sum4385671
Variance387.0805299
MonotonicityNot monotonic
2023-11-22T17:19:00.986245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3208
 
3.2%
43 2804
 
2.8%
44 2496
 
2.5%
45 2376
 
2.3%
38 2213
 
2.2%
40 2201
 
2.2%
46 2189
 
2.2%
41 2117
 
2.1%
42 2113
 
2.1%
47 2106
 
2.1%
Other values (108) 77943
76.6%
ValueCountFrequency (%)
1 3208
3.2%
2 1101
 
1.1%
3 668
 
0.7%
4 378
 
0.4%
5 286
 
0.3%
ValueCountFrequency (%)
132 1
< 0.1%
129 1
< 0.1%
126 1
< 0.1%
121 1
< 0.1%
120 1
< 0.1%

num_procedures
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339730362
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:01.082682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705806979
Coefficient of variation (CV)1.273246489
Kurtosis0.8571103021
Mean1.339730362
Median Absolute Deviation (MAD)1
Skewness1.316414763
Sum136339
Variance2.90977745
MonotonicityNot monotonic
2023-11-22T17:19:01.169556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
6 4954
 
4.9%
4 4180
 
4.1%
5 3078
 
3.0%
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
4 4180
 
4.1%
ValueCountFrequency (%)
6 4954
 
4.9%
5 3078
 
3.0%
4 4180
 
4.1%
3 9443
9.3%
2 12717
12.5%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02184423
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:01.285946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.127566209
Coefficient of variation (CV)0.5072803163
Kurtosis3.468154915
Mean16.02184423
Median Absolute Deviation (MAD)5
Skewness1.326672134
Sum1630479
Variance66.05733248
MonotonicityNot monotonic
2023-11-22T17:19:01.415526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6086
 
6.0%
12 6004
 
5.9%
11 5795
 
5.7%
15 5792
 
5.7%
14 5707
 
5.6%
16 5430
 
5.3%
10 5346
 
5.3%
17 4919
 
4.8%
9 4913
 
4.8%
18 4523
 
4.4%
Other values (65) 47251
46.4%
ValueCountFrequency (%)
1 262
 
0.3%
2 470
 
0.5%
3 900
0.9%
4 1417
1.4%
5 2017
2.0%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%

number_outpatient
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3693571527
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:01.541473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.267265097
Coefficient of variation (CV)3.431001911
Kurtosis147.9077363
Mean0.3693571527
Median Absolute Deviation (MAD)0
Skewness8.832958927
Sum37588
Variance1.605960825
MonotonicityNot monotonic
2023-11-22T17:19:01.652567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
Other values (29) 285
 
0.3%
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1978362125
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:01.756063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9304722684
Coefficient of variation (CV)4.703245461
Kurtosis1191.686726
Mean0.1978362125
Median Absolute Deviation (MAD)0
Skewness22.85558215
Sum20133
Variance0.8657786423
MonotonicityNot monotonic
2023-11-22T17:19:01.868779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
10 34
 
< 0.1%
Other values (23) 122
 
0.1%
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%

number_inpatient
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6355659061
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:01.974844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26286329
Coefficient of variation (CV)1.986990299
Kurtosis20.71939695
Mean0.6355659061
Median Absolute Deviation (MAD)0
Skewness3.614138992
Sum64679
Variance1.594823689
MonotonicityNot monotonic
2023-11-22T17:19:02.077453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
Other values (11) 194
 
0.2%
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 6
< 0.1%

diag_1
Text

Distinct716
Distinct (%)0.7%
Missing21
Missing (%)< 0.1%
Memory size5.8 MiB
2023-11-22T17:19:02.393175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.175664652
Min length1

Characters and Unicode

Total characters323108
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.1%

Sample

1st row250.83
2nd row276
3rd row648
4th row8
5th row197
ValueCountFrequency (%)
428 6862
 
6.7%
414 6581
 
6.5%
786 4016
 
3.9%
410 3614
 
3.6%
486 3508
 
3.4%
427 2766
 
2.7%
491 2275
 
2.2%
715 2151
 
2.1%
682 2042
 
2.0%
434 2028
 
2.0%
Other values (706) 65902
64.8%
2023-11-22T17:19:02.840923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (3) 10167
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 312941
96.9%
Other Punctuation 8522
 
2.6%
Uppercase Letter 1645
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 55457
17.7%
2 39876
12.7%
8 37949
12.1%
5 37131
11.9%
7 28668
9.2%
1 28106
9.0%
0 24960
8.0%
6 23198
7.4%
9 19978
 
6.4%
3 17618
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
V 1644
99.9%
E 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 8522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 321463
99.5%
Latin 1645
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 55457
17.3%
2 39876
12.4%
8 37949
11.8%
5 37131
11.6%
7 28668
8.9%
1 28106
8.7%
0 24960
7.8%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Latin
ValueCountFrequency (%)
V 1644
99.9%
E 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (3) 10167
 
3.1%

diag_2
Text

Distinct748
Distinct (%)0.7%
Missing358
Missing (%)0.4%
Memory size5.8 MiB
2023-11-22T17:19:03.200456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1738423
Min length1

Characters and Unicode

Total characters321853
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st row250.01
2nd row250
3rd row250.43
4th row157
5th row411
ValueCountFrequency (%)
276 6752
 
6.7%
428 6662
 
6.6%
250 6071
 
6.0%
427 5036
 
5.0%
401 3736
 
3.7%
496 3305
 
3.3%
599 3288
 
3.2%
403 2823
 
2.8%
414 2650
 
2.6%
411 2566
 
2.5%
Other values (738) 58519
57.7%
2023-11-22T17:19:03.731327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 51155
15.9%
2 49765
15.5%
5 38176
11.9%
0 34046
10.6%
8 28711
8.9%
7 28654
8.9%
1 26158
8.1%
9 21842
6.8%
6 19990
 
6.2%
3 14097
 
4.4%
Other values (3) 9259
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 312594
97.1%
Other Punctuation 6723
 
2.1%
Uppercase Letter 2536
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 51155
16.4%
2 49765
15.9%
5 38176
12.2%
0 34046
10.9%
8 28711
9.2%
7 28654
9.2%
1 26158
8.4%
9 21842
7.0%
6 19990
 
6.4%
3 14097
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
V 1805
71.2%
E 731
28.8%
Other Punctuation
ValueCountFrequency (%)
. 6723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 319317
99.2%
Latin 2536
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 51155
16.0%
2 49765
15.6%
5 38176
12.0%
0 34046
10.7%
8 28711
9.0%
7 28654
9.0%
1 26158
8.2%
9 21842
6.8%
6 19990
 
6.3%
3 14097
 
4.4%
Latin
ValueCountFrequency (%)
V 1805
71.2%
E 731
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 51155
15.9%
2 49765
15.5%
5 38176
11.9%
0 34046
10.6%
8 28711
8.9%
7 28654
8.9%
1 26158
8.1%
9 21842
6.8%
6 19990
 
6.2%
3 14097
 
4.4%
Other values (3) 9259
 
2.9%

diag_3
Text

MISSING 

Distinct789
Distinct (%)0.8%
Missing1423
Missing (%)1.4%
Memory size5.8 MiB
2023-11-22T17:19:04.083247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.141604297
Min length1

Characters and Unicode

Total characters315238
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st row255
2nd rowV27
3rd row403
4th row250
5th row250
ValueCountFrequency (%)
250 11555
 
11.5%
401 8289
 
8.3%
276 5175
 
5.2%
428 4577
 
4.6%
427 3955
 
3.9%
414 3664
 
3.7%
496 2605
 
2.6%
403 2357
 
2.3%
585 1992
 
2.0%
272 1969
 
2.0%
Other values (779) 54205
54.0%
2023-11-22T17:19:04.533728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 51244
16.3%
4 49252
15.6%
5 41260
13.1%
0 39711
12.6%
7 26504
8.4%
1 24684
7.8%
8 23825
7.6%
9 17323
 
5.5%
6 16441
 
5.2%
3 14333
 
4.5%
Other values (3) 10661
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304577
96.6%
Other Punctuation 5603
 
1.8%
Uppercase Letter 5058
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 51244
16.8%
4 49252
16.2%
5 41260
13.5%
0 39711
13.0%
7 26504
8.7%
1 24684
8.1%
8 23825
7.8%
9 17323
 
5.7%
6 16441
 
5.4%
3 14333
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
V 3814
75.4%
E 1244
 
24.6%
Other Punctuation
ValueCountFrequency (%)
. 5603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310180
98.4%
Latin 5058
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 51244
16.5%
4 49252
15.9%
5 41260
13.3%
0 39711
12.8%
7 26504
8.5%
1 24684
8.0%
8 23825
7.7%
9 17323
 
5.6%
6 16441
 
5.3%
3 14333
 
4.6%
Latin
ValueCountFrequency (%)
V 3814
75.4%
E 1244
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 51244
16.3%
4 49252
15.6%
5 41260
13.1%
0 39711
12.6%
7 26504
8.4%
1 24684
7.8%
8 23825
7.6%
9 17323
 
5.5%
6 16441
 
5.2%
3 14333
 
4.5%
Other values (3) 10661
 
3.4%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.422606765
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-11-22T17:19:04.642492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933600145
Coefficient of variation (CV)0.2605014931
Kurtosis-0.07905602427
Mean7.422606765
Median Absolute Deviation (MAD)1
Skewness-0.8767462388
Sum755369
Variance3.738809521
MonotonicityNot monotonic
2023-11-22T17:19:04.908124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 49474
48.6%
5 11393
 
11.2%
8 10616
 
10.4%
7 10393
 
10.2%
6 10161
 
10.0%
4 5537
 
5.4%
3 2835
 
2.8%
2 1023
 
1.0%
1 219
 
0.2%
16 45
 
< 0.1%
Other values (6) 70
 
0.1%
ValueCountFrequency (%)
1 219
 
0.2%
2 1023
 
1.0%
3 2835
 
2.8%
4 5537
5.4%
5 11393
11.2%
ValueCountFrequency (%)
16 45
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 16
 
< 0.1%
12 9
 
< 0.1%

max_glu_serum
Text

MISSING 

Distinct3
Distinct (%)0.1%
Missing96420
Missing (%)94.7%
Memory size3.3 MiB
2023-11-22T17:19:04.999908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters21384
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>300
2nd row>300
3rd rowNorm
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
norm 2597
48.6%
200 1485
27.8%
300 1264
23.6%
2023-11-22T17:19:05.190924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5498
25.7%
> 2749
12.9%
N 2597
12.1%
o 2597
12.1%
r 2597
12.1%
m 2597
12.1%
2 1485
 
6.9%
3 1264
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8247
38.6%
Lowercase Letter 7791
36.4%
Math Symbol 2749
 
12.9%
Uppercase Letter 2597
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5498
66.7%
2 1485
 
18.0%
3 1264
 
15.3%
Lowercase Letter
ValueCountFrequency (%)
o 2597
33.3%
r 2597
33.3%
m 2597
33.3%
Math Symbol
ValueCountFrequency (%)
> 2749
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 2597
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10996
51.4%
Latin 10388
48.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5498
50.0%
> 2749
25.0%
2 1485
 
13.5%
3 1264
 
11.5%
Latin
ValueCountFrequency (%)
N 2597
25.0%
o 2597
25.0%
r 2597
25.0%
m 2597
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5498
25.7%
> 2749
12.9%
N 2597
12.1%
o 2597
12.1%
r 2597
12.1%
m 2597
12.1%
2 1485
 
6.9%
3 1264
 
5.9%

A1Cresult
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing84748
Missing (%)83.3%
Memory size3.6 MiB
2023-11-22T17:19:05.286117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.586437889
Min length2

Characters and Unicode

Total characters44016
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>7
2nd row>7
3rd row>8
4th rowNorm
5th rowNorm
ValueCountFrequency (%)
8 8216
48.3%
norm 4990
29.3%
7 3812
22.4%
2023-11-22T17:19:05.501626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
> 12028
27.3%
8 8216
18.7%
N 4990
11.3%
o 4990
11.3%
r 4990
11.3%
m 4990
11.3%
7 3812
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14970
34.0%
Math Symbol 12028
27.3%
Decimal Number 12028
27.3%
Uppercase Letter 4990
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4990
33.3%
r 4990
33.3%
m 4990
33.3%
Decimal Number
ValueCountFrequency (%)
8 8216
68.3%
7 3812
31.7%
Math Symbol
ValueCountFrequency (%)
> 12028
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 4990
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24056
54.7%
Latin 19960
45.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4990
25.0%
o 4990
25.0%
r 4990
25.0%
m 4990
25.0%
Common
ValueCountFrequency (%)
> 12028
50.0%
8 8216
34.2%
7 3812
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
> 12028
27.3%
8 8216
18.7%
N 4990
11.3%
o 4990
11.3%
r 4990
11.3%
m 4990
11.3%
7 3812
 
8.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:05.583838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.732405715
Min length2

Characters and Unicode

Total characters278066
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 81778
80.4%
steady 18346
 
18.0%
up 1067
 
1.0%
down 575
 
0.6%
2023-11-22T17:19:05.782142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 176300
63.4%
Uppercase Letter 101766
36.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 82353
46.7%
t 18346
 
10.4%
e 18346
 
10.4%
a 18346
 
10.4%
d 18346
 
10.4%
y 18346
 
10.4%
p 1067
 
0.6%
w 575
 
0.3%
n 575
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 81778
80.4%
S 18346
 
18.0%
U 1067
 
1.0%
D 575
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 278066
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:05.858190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.05528369
Min length2

Characters and Unicode

Total characters209158
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 100227
98.5%
steady 1384
 
1.4%
up 110
 
0.1%
down 45
 
< 0.1%
2023-11-22T17:19:06.052497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107392
51.3%
Uppercase Letter 101766
48.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 100272
93.4%
t 1384
 
1.3%
e 1384
 
1.3%
a 1384
 
1.3%
d 1384
 
1.3%
y 1384
 
1.3%
p 110
 
0.1%
w 45
 
< 0.1%
n 45
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 100227
98.5%
S 1384
 
1.4%
U 110
 
0.1%
D 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 209158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:06.133212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.026472496
Min length2

Characters and Unicode

Total characters206226
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101063
99.3%
steady 668
 
0.7%
up 24
 
< 0.1%
down 11
 
< 0.1%
2023-11-22T17:19:06.327358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104460
50.7%
Uppercase Letter 101766
49.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101074
96.8%
t 668
 
0.6%
e 668
 
0.6%
a 668
 
0.6%
d 668
 
0.6%
y 668
 
0.6%
p 24
 
< 0.1%
w 11
 
< 0.1%
n 11
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101063
99.3%
S 668
 
0.7%
U 24
 
< 0.1%
D 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 206226
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:06.402339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.003124816
Min length2

Characters and Unicode

Total characters203850
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101680
99.9%
steady 79
 
0.1%
up 6
 
< 0.1%
down 1
 
< 0.1%
2023-11-22T17:19:06.596648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102084
50.1%
Uppercase Letter 101766
49.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101681
99.6%
t 79
 
0.1%
e 79
 
0.1%
a 79
 
0.1%
d 79
 
0.1%
y 79
 
0.1%
p 6
 
< 0.1%
w 1
 
< 0.1%
n 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101680
99.9%
S 79
 
0.1%
U 6
 
< 0.1%
D 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:06.684072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.187371028
Min length2

Characters and Unicode

Total characters222600
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 96575
94.9%
steady 4670
 
4.6%
up 327
 
0.3%
down 194
 
0.2%
2023-11-22T17:19:06.917000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120834
54.3%
Uppercase Letter 101766
45.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 96769
80.1%
t 4670
 
3.9%
e 4670
 
3.9%
a 4670
 
3.9%
d 4670
 
3.9%
y 4670
 
3.9%
p 327
 
0.3%
w 194
 
0.2%
n 194
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 96575
94.9%
S 4670
 
4.6%
U 327
 
0.3%
D 194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 222600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:06.999251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%
2023-11-22T17:19:07.194614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:07.276475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.45736297
Min length2

Characters and Unicode

Total characters250076
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowSteady
ValueCountFrequency (%)
no 89080
87.5%
steady 11356
 
11.2%
up 770
 
0.8%
down 560
 
0.6%
2023-11-22T17:19:07.475911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 148310
59.3%
Uppercase Letter 101766
40.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 89640
60.4%
t 11356
 
7.7%
e 11356
 
7.7%
a 11356
 
7.7%
d 11356
 
7.7%
y 11356
 
7.7%
p 770
 
0.5%
w 560
 
0.4%
n 560
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 89080
87.5%
S 11356
 
11.2%
U 770
 
0.8%
D 560
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 250076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:07.558394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.375606784
Min length2

Characters and Unicode

Total characters241756
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 91116
89.5%
steady 9274
 
9.1%
up 812
 
0.8%
down 564
 
0.6%
2023-11-22T17:19:07.751840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139990
57.9%
Uppercase Letter 101766
42.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 91680
65.5%
t 9274
 
6.6%
e 9274
 
6.6%
a 9274
 
6.6%
d 9274
 
6.6%
y 9274
 
6.6%
p 812
 
0.6%
w 564
 
0.4%
n 564
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 91116
89.5%
S 9274
 
9.1%
U 812
 
0.8%
D 564
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 241756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:07.827184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000904035
Min length2

Characters and Unicode

Total characters203624
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101743
> 99.9%
steady 23
 
< 0.1%
2023-11-22T17:19:08.016487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101858
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101743
99.9%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101743
> 99.9%
S 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:08.093940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.276516715
Min length2

Characters and Unicode

Total characters231672
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 94438
92.8%
steady 6976
 
6.9%
up 234
 
0.2%
down 118
 
0.1%
2023-11-22T17:19:08.291217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129906
56.1%
Uppercase Letter 101766
43.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 94556
72.8%
t 6976
 
5.4%
e 6976
 
5.4%
a 6976
 
5.4%
d 6976
 
5.4%
y 6976
 
5.4%
p 234
 
0.2%
w 118
 
0.1%
n 118
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 94438
92.8%
S 6976
 
6.9%
U 234
 
0.2%
D 118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 231672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:08.369277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.241475542
Min length2

Characters and Unicode

Total characters228106
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 95401
93.7%
steady 6100
 
6.0%
up 178
 
0.2%
down 87
 
0.1%
2023-11-22T17:19:08.560965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126340
55.4%
Uppercase Letter 101766
44.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 95488
75.6%
t 6100
 
4.8%
e 6100
 
4.8%
a 6100
 
4.8%
d 6100
 
4.8%
y 6100
 
4.8%
p 178
 
0.1%
w 87
 
0.1%
n 87
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 95401
93.7%
S 6100
 
6.0%
U 178
 
0.2%
D 87
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 228106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:08.638446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.011654187
Min length2

Characters and Unicode

Total characters204718
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101458
99.7%
steady 295
 
0.3%
up 10
 
< 0.1%
down 3
 
< 0.1%
2023-11-22T17:19:08.833606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102952
50.3%
Uppercase Letter 101766
49.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101461
98.6%
t 295
 
0.3%
e 295
 
0.3%
a 295
 
0.3%
d 295
 
0.3%
y 295
 
0.3%
p 10
 
< 0.1%
w 3
 
< 0.1%
n 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101458
99.7%
S 295
 
0.3%
U 10
 
< 0.1%
D 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 204718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:08.910187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001316746
Min length2

Characters and Unicode

Total characters203666
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101728
> 99.9%
steady 31
 
< 0.1%
down 5
 
< 0.1%
up 2
 
< 0.1%
2023-11-22T17:19:09.100547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101900
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101733
99.8%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
w 5
 
< 0.1%
n 5
 
< 0.1%
p 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101728
> 99.9%
S 31
 
< 0.1%
D 5
 
< 0.1%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:09.172109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000117918
Min length2

Characters and Unicode

Total characters203544
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101763
> 99.9%
steady 3
 
< 0.1%
2023-11-22T17:19:09.360351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101778
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101763
> 99.9%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101763
> 99.9%
S 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:09.438242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.001493623
Min length2

Characters and Unicode

Total characters203684
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101727
> 99.9%
steady 38
 
< 0.1%
up 1
 
< 0.1%
2023-11-22T17:19:09.633101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101918
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101727
99.8%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
p 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101727
> 99.9%
S 38
 
< 0.1%
U 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

examide
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:09.694089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101766
100.0%
2023-11-22T17:19:09.852593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101766
50.0%
Lowercase Letter 101766
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 101766
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203532
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

citoglipton
Text

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:09.915370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101766
100.0%
2023-11-22T17:19:10.072171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101766
50.0%
Lowercase Letter 101766
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 101766
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203532
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101766
50.0%
o 101766
50.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
2023-11-22T17:19:10.172158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length3.45266592
Min length2

Characters and Unicode

Total characters351364
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowNo
4th rowUp
5th rowSteady
ValueCountFrequency (%)
no 47383
46.6%
steady 30849
30.3%
down 12218
 
12.0%
up 11316
 
11.1%
2023-11-22T17:19:10.390971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249598
71.0%
Uppercase Letter 101766
29.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 59601
23.9%
t 30849
12.4%
e 30849
12.4%
a 30849
12.4%
d 30849
12.4%
y 30849
12.4%
w 12218
 
4.9%
n 12218
 
4.9%
p 11316
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N 47383
46.6%
S 30849
30.3%
D 12218
 
12.0%
U 11316
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 351364
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:10.466288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.027317572
Min length2

Characters and Unicode

Total characters206312
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101060
99.3%
steady 692
 
0.7%
up 8
 
< 0.1%
down 6
 
< 0.1%
2023-11-22T17:19:10.660997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104546
50.7%
Uppercase Letter 101766
49.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101066
96.7%
t 692
 
0.7%
e 692
 
0.7%
a 692
 
0.7%
d 692
 
0.7%
y 692
 
0.7%
p 8
 
< 0.1%
w 6
 
< 0.1%
n 6
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101060
99.3%
S 692
 
0.7%
U 8
 
< 0.1%
D 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 206312
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:10.738765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000510976
Min length2

Characters and Unicode

Total characters203584
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101753
> 99.9%
steady 13
 
< 0.1%
2023-11-22T17:19:10.927216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101818
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101753
99.9%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101753
> 99.9%
S 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:10.999245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%
2023-11-22T17:19:11.366661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:11.447071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000078612
Min length2

Characters and Unicode

Total characters203540
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101764
> 99.9%
steady 2
 
< 0.1%
2023-11-22T17:19:11.637473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101774
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101764
> 99.9%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101764
> 99.9%
S 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:11.713580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.000039306
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%
2023-11-22T17:19:11.899913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

change
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-22T17:19:11.988383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowNo
4th rowCh
5th rowCh
ValueCountFrequency (%)
no 54755
53.8%
ch 47011
46.2%
2023-11-22T17:19:12.177011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101766
50.0%
Lowercase Letter 101766
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 54755
53.8%
C 47011
46.2%
Lowercase Letter
ValueCountFrequency (%)
o 54755
53.8%
h 47011
46.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 203532
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:12.256399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.770031248
Min length2

Characters and Unicode

Total characters281895
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes 78363
77.0%
no 23403
 
23.0%
2023-11-22T17:19:12.431151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180129
63.9%
Uppercase Letter 101766
36.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 78363
43.5%
s 78363
43.5%
o 23403
 
13.0%
Uppercase Letter
ValueCountFrequency (%)
Y 78363
77.0%
N 23403
 
23.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281895
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 78363
27.8%
e 78363
27.8%
s 78363
27.8%
N 23403
 
8.3%
o 23403
 
8.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
2023-11-22T17:19:12.522007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.460880844
Min length2

Characters and Unicode

Total characters250434
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO
ValueCountFrequency (%)
no 54864
53.9%
30 46902
46.1%
2023-11-22T17:19:12.713446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 109728
43.8%
Decimal Number 93804
37.5%
Math Symbol 46902
18.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 54864
50.0%
O 54864
50.0%
Decimal Number
ValueCountFrequency (%)
3 46902
50.0%
0 46902
50.0%
Math Symbol
ValueCountFrequency (%)
> 35545
75.8%
< 11357
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 140706
56.2%
Latin 109728
43.8%

Most frequent character per script

Common
ValueCountFrequency (%)
3 46902
33.3%
0 46902
33.3%
> 35545
25.3%
< 11357
 
8.1%
Latin
ValueCountFrequency (%)
N 54864
50.0%
O 54864
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%